System program of a wireless coverage prediction

ABSTRACT

This invention uses multi-tier indexing methods to organize the wireless communication industry standard Radio Resource Management (RRM) parameters, compression techniques to compress the indexed RRM parameters, model the RRM parameters to identify the relationships between the parameters, simulate the model by eliminating predefined non-influential parameters, to conclude the signal-noise-ratio values in order to determine signal coverage. This invention is used to replace the Road Tests currently implemented by the service carriers for determining actual service coverage.

RELATED APPLICATION

The present invention is a continuation application of and claims apriority to the U.S. patent application Ser. No. 11/549,966, filed onOct. 16, 2006, and is herein incorporated in its entirety by referencefor all purposes.

FIELD OF INVENTION

This invention relates to a system for measuring and ensuring wirelesscommunications coverage at various geographic areas where the servicecarriers provide its communication services. The coverage of a cellularsystem depends on many different factors including geographicalobstacles, traffic load, signal interferences, handoff, and others.Therefore, the coverage of a cellar system varies depending on differentfactors as mentioned previously. The current system collects andanalyzes real communication traffic data for modeling and simulations toconclude the quality of signals in terms of signal-to-noise ratio (SNR)to determine its coverage.

BACKGROUND OF THE INVENTION

Signal coverage is a major service concern to all wireless communicationsubscribers as well as the service carriers. The subscribers have toroam from one place to another in order to obtain a better signalcoverage for his desired communications. The subscriber cannot predictany location where provides expected or poor signal coverage. The systemand environmental factors that affect signal coverage change dynamicallythrough time period. The service carriers in the wireless communicationindustry have implemented the road tests by sending technicians out tothe fields to detect and record real coverage signals. The techniciansuse various signal detecting equipments (i.e., cell phone, globalpositioning system, and personal computers) to record live signalstrengths at different geographical locations. The collected signal datawill be analyzed at a later time to determine the field coverage. Thisroad tests have been tedious, time consuming, inaccurate due to humanfactors, and costly tasks.

The current invention is for determining cell coverage withoutperforming the road tests repeatedly as the service carriers perform innowadays. This invention implements a series of indexing, modeling, andsimulations on the standard Radio Resource Management (RRM) parametersthat are available on the wireless communication systems. By determiningthe influential relationships between all of the RRM factors and in viewof a baseline road test data, this invention concludes a signal-to-noiseratio (SNR) value to determine the signal coverage for a desiredcoverage location.

This invention will save not only costs for the service carriers toperform road tests but also improves the accuracy of determining filedsignal coverage in a timely manner. The service carriers therefore canimprove its service coverage in a much more efficient method.

SUMMARY OF THE INVENTION

This invention implements a series of indexing, modeling, andsimulations on the RRM parameters that are available on the wirelesscommunication systems to determine filed signal coverage.

There are five (5) modules performing various tasks of the currentinvention. The five modules are Definition Module, Index Module,Characterizing Module, Modeler Module, and Simulator Module.

The “Definition Module” defines the conformations and relationshipsbetween vendor-specific communication traffic data and the standard RRMparameters.

The “Index Module” indexes all RRM parameters by multiple-tier indexingmethods for the efficiencies of data access and data storages.

The “Characterizing Module” defines the characteristic elements of eachRRM parameter by a mathematical expression for the later modeling andsimulations processes.

The “Modeler Module” sets a model by all of the RRM parameters torepresent influential relationships between each other and its impact onthe system coverage.

The “Simulator Module” repeats simulations by using the model that isset by the Modeler Module. The simulations eliminates RRM parametersthat are unessential per predefined requirements in order to determinesignal-coverage determining parameters. In view of a baseline SNR thathas been established by a road test data, and the fact of the industrystandards that the RRM parameters are designed to balance the systemcoverage, the SNR reports are therefore concluded by end of thesimulations when only the essential parameters are considered.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a process flow of the current invention.

FIG. 2 is a system architecture of the current invention.

FIG. 3 is an example of the SNR report.

FIG. 4 is an example of the SNR report.

DETAIL DESCRIPTIONS OF THE INVENTION Terminology and Lexicography

Multiple-Tier Indexing: Indexing on the data and its associated indicesthat were created by a previous tier indexing process. By the indexing,certain data storage may be saved by eliminating repeated data in orderto achieve the goals of data compressions.Radio Resource Management (RRM): The RRM refers to all RRM parametersdefined by the Universal Mobile Telecommunication System (UMTS)standard, or all Selection/Distribution Unit parameters defined by theCode Division Multiple Access 2000 (CDMA2000) standard, or parametersaffecting communication signal coverage that are defined by servicecarriers.Modeling: A process of generating an abstract model that usesmathematical expressions to describe the behavior of a system.Simulation: The process of creating imitative representations of atarget system that is modeled by mathematical expressions.Baseline signal-to-noise Ratio (BSNR): An SNR data collected from a roadtest that represent a worst coverage signal strength or an averagecoverage signal strength. Other SNR data that is collected from a roadtest can also be used as a baseline SNR at the invention operator'schoice.R-Tree Indexing: Tree data structures used for spatial access methodsi.e., for indexing multi-dimensional information; for example, the (X,Y) coordinates of geographical data. The data structure splits spacewith hierarchically nested, and possibly overlapping, boxes. Each nodeof an R-tree has a variable number of entries (up to some pre-definedmaximum). Each entry within a non-leaf node stores two pieces of data; away of identifying a child node, and the bounding box of all entrieswithin this child node.Move-To-Front (MTF) indexing: The MTF indexing is an encoding of data(typically a stream of bytes) designed to improve the performance ofentropy encoding techniques of compression. Each byte value is encodedby its index in a list, which changes over the course of the algorithm.The list is initially in order by byte value (0, 1, 2, 3, . . . , 255).Therefore, the first byte is always encoded by its own value. Afterencoding a byte, that value is moved to the front of the list beforecontinuing to the next byte.

-   -   Run-length Indexing: Run-length encoding is a form of data        compression in which runs of data (that is, sequences in which        the same data value occurs in many consecutive data elements)        are stored as a single data value and count, rather than as the        original run.    -   Huffman Indexing Huffman coding is an entropy encoding algorithm        used for lossless data compression. The term refers to the use        of a variable-length code table for encoding a source symbol        (such as a character in a file) where the variable-length code        table has been derived in a particular way based on the        estimated probability of occurrence for each possible value of        the source symbol.        Modeling: A process of generating an abstract model that uses        mathematical language to describe the behavior of a system.        Signal-to-noise ratio (SNR): an electrical engineering concept        defined as the ratio of a given transmitted signal to the        background noise of the transmission medium.        Indexing: A method of applying an integer and a symbol to        identify an array element, or a data structure which enables        fast lookup

According to the wireless communication standards, for example, but notlimited to the Universal Mobil Telecommunication System (UMTS) and CodeDivision Multiple Access 2000 (CDMA2000), RRM parameters are dedicatedto guarantee system quality and maintain the system performance. The RRMprovides functions including power control, handover, admission control,load control, packet switching, and resource management. However, noneof these functions provides an indication of signal coverage for aspecific cell location.

Before implementing this invention 10, a baseline road test 20 shall beperformed in order to identify the baseline SNR (BSNR) within a wirelesscommunication sector. This baseline SNR is used along with other RRMparameter data 42 that are available on the wireless communicationsystem for the modeling and simulation processes.

The system 10 of this invention includes five (5) modules which areDefinition 22, Index 24, Characterizing 26, Modeler 28, and Simulator30. The detail functions of each module follow.

This invention first analyzes all RRM parameters available from eitherbase station, base station controller (BSC), network management system(NMS), or from a centralized system archive. The interfaces ofretrieving the RRM parameters is a design issue depending on preferencesand configurations of each service carrier.

Once the RRM parameters are collected, the system, by the DefinitionModule, organizes the collected parameter data according to a predefinedvendor-specific definition. Due to different system vendorimplementations, the standard RRM parameters may be implemented indifferent methods or format. Therefore, the Definition Module identifiesand defines RRM parameters by the pre-determined vendor-specificdefinitions. Furthermore, any non-standard RRM parameters that theservice carrier deems to be signal-coverage-affecting factors can bedefined in the Definition Module.

When the RRM parameters are identified, the Index Module indexes the RRMparameter data. Due to the large amount of RRM parameter data, the IndexModule implements multiple-tier indexing methods. The RRM parameters arefirst indexed by the Replica-Tree indexing method. The amount of datafrom the first-tier indexing is still considered to be large from theefficiency point-of-view for data access and storage. The Index Moduletherefore applies a additional tiers indexing methods to the data andassociated indices from the first-tier indexing. The multiple-tierindexing methods after the first-tier indexing, in sequence order,include Move-To-Front (MTF) indexing methods, Run-length Indexingmethod, and Huffman Indexing Method.

The Characterizing Module characterizes each RRM parameter in terms ofeach parameter's characteristic elements by the following mathematicalexpression. The process of characterizing RRM parameters is to definethe detail influential elements of each RRM parameter.

V={RRM0, RRM1, RRM2, . . . RRMq, BSNR}

where

-   -   BSNR: baseline SNR    -   RRMq: q^(th) number of RRM parameter    -   V_(i)̂j: V_(i) to the jth power; An array of RRM parameters and        a baseline SNR

$F_{i} = \begin{bmatrix}{{Vi}\hat{}0} \\{{Vi}\hat{}1} \\{{Vi}^{\bigwedge}2} \\\ldots \\{{Vi}\hat{}j} \\{{Vi} \star {\sin (R)}} \\{{Vi} \star {\sin \left( {2\; R} \right)}} \\\ldots \\{{Vi} \star {\sin ({mR})}}\end{bmatrix}$

where

-   -   F_(i): characterizing array for the i^(th) member in array V    -   0≦i≦q

M_(i)=(V_(i),t0 V_(i),t1 V_(i),t2 . . . V_(i),tk)

where

-   -   tk: timepoint of k    -   M_(i): Array of sampling for RRM_(i) by K samples at different        timepoints

P_(i)=(F_(i),t0 F_(i),t1 F_(i),t2 . . . F_(i),tk)

where P_(i): characterizing array for RRM_(i) for all k timepoints

The Modeling Module sets a coverage environment model in terms of theRRM parameters for the purpose of simulations. The modeling processesinclude steps by using the following mathematical expressions.

The first step, by knowing Pan and M_(i), is to determine the W_(i) inthe following mathematical expression.

$M_{i} = {W_{i} \star \begin{bmatrix}{{Pa}\; 0} \\{{Pa}\; 1} \\{{Pa}\; 2} \\\ldots \\{Pan}\end{bmatrix}}$

Once the W_(i) is determined, the second step is to determine the R_(i)in the following mathematical expression.

$M_{i} = {W_{i} \star \left\{ {\begin{bmatrix}{{Pa}\; 0} \\{{Pa}\; 1} \\{{Pa}\; 2} \\\ldots \\{Pan}\end{bmatrix} + {Ri}} \right\}}$

where

-   -   M_(i): relationships array representing the relationships        between the RRM_(i) and all other RRM parameters    -   W_(i): an intermediate factor    -   P_(an): characterizing array for RRM_(an) for all k timepoints    -   R_(i): probability array for each Pan.    -   0≦a0 . . . an ≦q, and a0 . . . an≠i    -   a0≠a1≠a2≠ . . . ≠an

Multiple iterations of the above modeling processes are performed inorder to eliminate any Pan whose associated probability is less than 0.5(R_(i)<0.5).

Upon the RRM parameters' influential probabilities are all within apredetermined requirement, for example, smaller than 0.5, the modelingprocesses are terminated.

The Simulation Module simulates the RRM parameters' influences amongeach other by using the following mathematical expressions.

$C = {U \star \begin{bmatrix}{{Fb}\; 0} \\{{Fb}\; 1} \\{{Fb}\; 2} \\\ldots \\{Fbu} \\{{{Fc}\; 0} \star {{Fd}\; 0}} \\{{{Fc}\; 1} \star {{Fd}\; 1}} \\\ldots \\{{Fcy} \star {Fdy}} \\{{Fe}\; {0/{Ff}}\; 0} \\{{Fe}\; {1/{Ff}}\; 1} \\\ldots \\{{Fev}/{Ffv}}\end{bmatrix}}$

where

-   -   C: a constant (any number)    -   0≦b0 . . . bn≦q, and b0 . . . bu≠i    -   c0≠c1≠ . . . ≠cy    -   d0≠d1 ≠ . . . ≠dy    -   e0≠e1≠ . . . ≠ev    -   f0≠f1≠ . . . ≠fv    -   U: Balancing array to balance the influential RRMs in the        communication environment

The simulations begins by determining the U array based on theassumption that all influential RRM parameters should balance the signalcoverage by adjusting the RRM parameter values itself. When the U arrayis determined, different simulations among the influential RRMparameters may be performed in order to determine the SNR values of thecharacterizing array (F).

The model with the final list of RRM parameters is a representativemodel of the communication coverage environment. The SNR reportstherefore generated based on the simulations to indicate communicationsignal coverage.

It is to be understood that the embodiments and variations shown anddescribed herein are merely illustrative of the principles of thisinvention and that various modifications may be implemented by thoseskilled in the art without departing from the scope and spirit of theinvention.

1. A computer-readable medium having computer-executable instructions ofautomatic coverage preditions for wireless communications comprising:instructing a definition module to define radio resource managementparameters; instructing a modeling module to create models by firstmathematical expressions in terms of the resource management parameters;instructing a simulation module to perform simulations by using radioresource management parameters and a baseline signal-to-noise ratio(SNR) value; and instructing a characterizing module to definecharacterizations of the radio resource management parameters by secondmathematical expressions,V={RRM0, RRM1, RRM2, . . . RRMq, BSNR} where BSNR: baseline SNR RRMq:q^(th) number of RRM parameters $F_{i} = \begin{bmatrix}{{Vi}\hat{}0} \\{{Vi}\hat{}1} \\{{Vi}\hat{}2} \\\ldots \\{{Vi}\hat{}j} \\{{Vi} \star {\sin (R)}} \\{{Vi} \star {\sin \left( {2\; R} \right)}} \\\ldots \\{{Vi} \star {\sin ({mR})}}\end{bmatrix}$ where V_(i)̂j: V_(i) to the j^(th) power; An array of RRMparameters and a baseline SNR F_(i): characterizing array for the i^(th)member in array VM_(i)=(V_(i),t0 V_(i),t1 V_(i),t2 . . . V_(i),tk) where tk: timepoint ofk M_(i): Array of sampling for RRM_(i) by K samples at differenttimepointsP_(i)=(F_(i),t0 F_(i),t1 F_(i),t2 . . . F_(i),tk) where P_(i):characterizing array for RRM_(i) at k timepoints
 2. Thecomputer-readable medium having computer-executable instructions ofautomatic coverage preditions for wireless communications of claim 1comprising: instructing an index module to perform multiple-tierindexing on the radio resource management parameters.
 3. Thecomputer-readable medium having computer-executable instructions ofautomatic coverage preditions for wireless communications of claim 2comprising: the multiple-tier indexing includes Replica-tree indexingmethod and Move-To-Front (MTF) indexing method and Run-length Indexingmethod and Huffman Indexing methods.
 4. The computer-readable mediumhaving computer-executable instructions of automatic coverage preditionsfor wireless communications of claim 1 comprising: the firstmathematical expressions are,$M_{i} = {W_{i} \star \left\{ {\begin{bmatrix}{{Pa}\; 0} \\{{Pa}\; 1} \\{{Pa}\; 2} \\\ldots \\{Pan}\end{bmatrix} + {Ri}} \right\}}$ where M_(i): relationships arrayrepresenting the relationships between the RRM_(i) and all other RRMparameters W_(i): an intermediate factor P_(an): characterizing arrayfor RRM_(an) for all k timepoints R_(i): probability array for each Pan.0≦a0 . . . an ≦q, and a0 . . . an≠i a0≠a1≠a2≠ . . . ≠an
 5. Thecomputer-readable medium having computer-executable instructions ofautomatic coverage preditions for wireless communications of claim 1comprising: instructing the simulations being performed in accordancewith third mathematical expressions, $C = {U \star \begin{bmatrix}{{Fb}\; 0} \\{{Fb}\; 1} \\{{Fb}\; 2} \\\ldots \\{Fbu} \\{{{Fc}\; 0} \star {{Fd}\; 0}} \\{{{Fc}\; 1} \star {{Fd}\; 1}} \\\ldots \\{{Fcy} \star {Fdy}} \\{{Fe}\; {0/{Ff}}\; 0} \\{{Fe}\; {1/{Ff}}\; 1} \\\ldots \\{{Fev}/{Ffv}}\end{bmatrix}}$ where C: a constant (any number) 0≦b0 . . . bn ≦q, andb0 . . . bu≠i c0≠c1≠ . . . ≠cy d0≠d1≠ . . . ≠dy e0≠e1≠ . . . ev f0≠f1≠ .. . ≠fv U: Balancing array to balance the influential RRMs in thecommunication environment
 6. A computer-readable medium havingcomputer-executable instructions of automatic coverage preditions forwireless communications comprising: instructing a definition module todefine radio resource management parameters; and instructing an indexmodule to perform multiple-tier indexing on the radio resourcemanagement parameters, wherein the multiple-tier indexing includeReplica-tree indexing method and Move-To-Front (MTF) indexing method andRun-length Indexing method and Huffman Indexing methods.
 7. Thecomputer-readable medium having computer-executable instructions ofautomatic coverage preditions for wireless communications of claim 6comprising: instructing a modeling module to create models by firstmathematical expressions in terms of the resource management parameters.8. The computer-readable medium having computer-executable instructionsof automatic coverage preditions for wireless communications of claim 7comprising: instructing a simulation module to perform simulations byusing radio resource management parameters and a baselinesignal-to-noise ratio (SNR) value; and instructing a characterizingmodule to define characterizations of the radio resource managementparameters by second mathematical expressions,V={RRM1, RRM1, RRM2, . . . RRMq, BSNR} where BSNR: baseline SNR RRMq:q^(th) number of RRM parameters $F_{i} = \begin{bmatrix}{{Vi}\hat{}0} \\{{Vi}\hat{}1} \\{{Vi}\hat{}2} \\\ldots \\{{Vi}\hat{}j} \\{{Vi} \star {\sin (R)}} \\{{Vi} \star {\sin \left( {2\; R} \right)}} \\\ldots \\{{Vi} \star {\sin ({mR})}}\end{bmatrix}$ where V_(i)̂j: V_(i) to the j^(th) power; An array of RRMparameters and a baseline SNR F_(i): characterizing array for the i^(th)member in array VM_(i)=(V_(i),t0 V_(i),t1 V_(i),t2 . . . V_(i),tk) where tk: timepoint ofk M_(i): Array of sampling for RRM_(i) by K samples at differenttimepointsP_(i)=(F_(i),t0 F_(i),t1 F_(i),t2 . . . F_(i),tk) where P_(i):characterizing array for RRM_(i) at k timepoints
 9. Thecomputer-readable medium having computer-executable instructions ofautomatic coverage preditions for wireless communications of claim 8comprising: instructing the simulations being performed in accordancewith third mathematical expressions $C = {U \star \begin{bmatrix}{{Fb}\; 0} \\{{Fb}\; 1} \\{{Fb}\; 2} \\\ldots \\{Fbu} \\{{{Fc}\; 0} \star {{Fd}\; 0}} \\{{{Fc}\; 1} \star {{Fd}\; 1}} \\\ldots \\{{Fcy} \star {Fdy}} \\{{Fe}\; {0/{Ff}}\; 0} \\{{Fe}\; {1/{Ff}}\; 1} \\\ldots \\{{Fev}/{Ffv}}\end{bmatrix}}$ where C: a constant (any number) 0≦b0 . . . bn ≦q, andb0 . . . bu≠i c0≠c1≠ . . . ≠cy d0≠d1 . . . ≠dy e0≠e1≠ . . . ≠ev f0≠f1≠ .. . ≠fv U: Balancing array to balance the influential RRMs in thecommunication environment
 10. A computer-readable medium havingcomputer-executable instructions of automatic coverage preditions forwireless communications comprising: instructing a definition module todefine radio resource management parameters; and instructing acharacterizing module to define characterizations of the radio resourcemanagement parameters by second mathematical expressions,V={RRM0, RRM1, RRM2, . . . RRMq, BSNR} where BSNR: baseline SNR RRMq:q^(th) number of RRM parameters $F_{i} = \begin{bmatrix}{{Vi}\hat{}0} \\{{Vi}\hat{}1} \\{{Vi}\hat{}2} \\\ldots \\{{Vi}\hat{}j} \\{{Vi} \star {\sin (R)}} \\{{Vi} \star {\sin \left( {2\; R} \right)}} \\\ldots \\{{Vi} \star {\sin ({mR})}}\end{bmatrix}$ where V_(i)̂j: V_(i) to the jth power; An array of RRMparameters and a baseline SNR F_(i): characterizing array for the i^(th)member in array VM_(i)=(V_(i),t0 V_(i),t1, t2 . . . V_(i),tk) where tk: timepoint of kM_(i): Array of sampling for RRM_(i) by K samples at differenttimepointsP_(i)=(F_(i),t0 F_(i),t1 F_(i),t2 . . . F_(i),tk) where P_(i):characterizing array for RRM_(i) at k timepoints
 11. Thecomputer-readable medium having computer-executable instructions ofautomatic coverage preditions for wireless communications of claim 10comprising: instructing a modeling module to create models by firstmathematical expressions in terms of the resource management parameters;and instructing an index module to perform multiple-tier indexing on theradio resource management parameters.
 12. The computer-readable mediumhaving computer-executable instructions of automatic coverage preditionsfor wireless communications of claim 11 comprising: the multiple-tierindexing includes Replica-tree indexing method and Move-To-Front (MTF)indexing method and Run-length Indexing method and Huffman Indexingmethods.
 13. The computer-readable medium having computer-executableinstructions of automatic coverage preditions for wirelesscommunications of claim 11 comprising: the first mathematicalexpressions are, $M_{i} = {W_{i} \star \left\{ {\begin{bmatrix}{{Pa}\; 0} \\{{Pa}\; 1} \\{{Pa}\; 2} \\\ldots \\{Pan}\end{bmatrix} + {Ri}} \right\}}$ where M_(i): relationships arrayrepresenting the relationships between the RRM_(i) and all other RRMparameters W_(i): an intermediate factor P_(an): characterizing arrayfor RRM_(an) for all k timepoints R_(i): probability array for each Pan.0≦a0 . . . an ≦q, and a0 . . . an≠i a0≠a1≠a2≠ . . . ≠an
 14. Thecomputer-readable medium having computer-executable instructions ofautomatic coverage preditions for wireless communications of claim 11comprising: instructing a simulation module to perform simulations byusing radio resource management parameters and a baselinesignal-to-noise ratio (SNR) value.
 15. The computer-readable mediumhaving computer-executable instructions of automatic coverage preditionsfor wireless communications of claim 14 comprising: instructing thesimulations being performed in accordance with third mathematicalexpressions, $C = {U \star \begin{bmatrix}{{Fb}\; 0} \\{{Fb}\; 1} \\{{Fb}\; 2} \\\ldots \\{Fbu} \\{{{Fc}\; 0} \star {{Fd}\; 0}} \\{{{Fc}\; 1} \star {{Fd}\; 1}} \\\ldots \\{{Fcy} \star {Fdy}} \\{{Fe}\; {0/{Ff}}\; 0} \\{{Fe}\; {1/{Ff}}\; 1} \\\ldots \\{{Fev}/{Ffv}}\end{bmatrix}}$ where C: a constant (any number) 0≦b0 . . . bn ≦q, andb0 . . . bu≠i c0≠c1≠ . . . ≠cy d0≠d1≠ . . . ≠dy e0≠e1≠ . . . ≠ev f0≠f1≠. . . ≠fv U: Balancing array to balance the influential RRMs in thecommunication environment